The present disclosure relates generally to sentiment modeling and in particular to spatial analysis of social media sentiment data.
The advent of social media has led to an explosion of data from various platforms, including blogs, online forums, Facebook®, and Twitter®, as well as other social media channels. As such, social media has become not only a powerful tool for consumers to voice their opinions and concerns, but an equally powerful tool for businesses to gain a better understanding of consumers' likes and dislikes. Moreover, social media data provide businesses with a wealth of free publicity and exposure. On the other hand, when social media turns negative (e.g., bad reviews), the repercussions of such negative exposure can be detrimental to businesses. In an effort to enhance the experience and thereby minimize negative social media, businesses have begun to analyze social media sentiments (i.e., public opinion as to how consumers feel (e.g., happy, mad, angry, sad, frustrated, excited, funny, etc.) to better understand consumer preferences, market trends and brand awareness. For example, if the social sentiment of an individual or group of individuals is negative, a business can address the problem before it grows.